Evolving intraday foreign exchange trading strategies utilizing multiple instruments price series

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Simone Cirillo and Stefan Lloyd and Peter Nordin",
  title =        "Evolving intraday foreign exchange trading strategies
                 utilizing multiple instruments price series",
  howpublished = "ArXiv",
  year =         "2014",
  volume =       "abs/1411.2153",
  month =        "8 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://arxiv.org/abs/1411.2153",
  timestamp =    "Fri, 03 Jun 4433701 16:53:52 +",
  biburl =       "http://dblp.uni-trier.de/rec/bib/journals/corr/CirilloLN14",
  bibsource =    "dblp computer science bibliography, http://dblp.org",
  size =         "15 pages",
  abstract =     "We propose a Genetic Programming architecture for the
                 generation of foreign exchange trading strategies. The
                 system's principal features are the evolution of
                 free-form strategies which do not rely on any prior
                 models and the use of price series from multiple
                 instruments as input data. This latter feature
                 constitutes an innovation with respect to previous
                 works documented in literature. In this article we use
                 Open, High, Low, Close bar data at a 5 minutes
                 frequency for the AUD.USD, EUR.USD, GBP.USD and USD.JPY
                 currency pairs. We will test the implementation
                 analysing the in-sample and out-of-sample performance
                 of strategies for trading the USD.JPY obtained across
                 multiple algorithm runs. We will also evaluate the
                 differences between strategies selected according to
                 two different criteria: one relies on the fitness
                 obtained on the training set only, the second one makes
                 use of an additional validation dataset. Strategy
                 activity and trade accuracy are remarkably stable
                 between in and out of sample results. From a
                 profitability aspect, the two criteria both result in
                 strategies successful on out-of-sample data but
                 exhibiting different characteristics. The overall best
                 performing out-of-sample strategy achieves a yearly
                 return of 19percent.",

Genetic Programming entries for Simone Cirillo Stefan Lloyd Peter Nordin